Modeling of the Microvascular Pulse for Tracking the Vasoconstriction Response to Deep Inspiratory Gasp

This work demonstrates the suitability of a microvascular pulse decomposition algorithm (PDA) for evaluating the vasoconstriction response to a deep inspiratory gasp (DIG). Synchronous ECG, respiratory, and laser Doppler flowmetry (LDF) signals of 13 healthy subjects (age: 26 \( \pm \) 3 years) were analyzed, and a four-Gaussian PDA was applied to reconstruct the LDF heartbeat pulsations. To assess the tracking of the transient vasoconstriction, the goodness-of-fit achieved during the DIG was compared with the performance at baseline. Moreover, the heart rate (HR) derived from the model’s systolic component was validated against the ECG, comparing the agreement with the one obtained from the wavelet transform analysis (WTA) of the LDF signal. The model’s normalized root-mean-square error and average \( {\text{R}}^{2} \) did not decrease during the DIG (p = 0.249, p = 0.552), and a nearly optimal pulse modeling accuracy was maintained (p = 0.286). Furthermore, the proposed PDA could better reproduce the reference HR than WTA, with a 46.8% reduction of the median root-mean-square error (p < 0.001), which did not worsen during the DIG (p = 0.861). Therefore, this method might find valuable application in the evaluation of neurovascular deterioration.